{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "DrillStringDesign.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNTEMHK59PViY9NU0DTfTKx",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Divyanshu-ISM/Oil-and-Gas-data-analysis/blob/master/DrillStringDesign.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oZIFbbWYIVDT",
        "colab_type": "text"
      },
      "source": [
        "#Drill String Design with Python\n",
        "\n",
        "This is a beginner level code that explains how we can define the stress state of a drill string and hence design a competent DS. \n",
        "\n",
        "Ref. Applied Drilling Engineering by Bourgoyne Jr. (SPE)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "huAdKyhD5cL-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SRkMsRQt6bLB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "L_dp = 10000 #ft\n",
        "\n",
        "w_dp = 19.5 #lbm/ft\n",
        "\n",
        "L_dc = 600 #ft\n",
        "\n",
        "w_dc = 147 #lbm/ft\n",
        "\n",
        "mud_w = 15 #lbm/gal or ppg"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UHTZ95_p7Kp2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "BF = 1 - (15/65.5)\n",
        "\n",
        "W_air = L_dp*w_dp + L_dc*w_dc\n",
        "\n",
        "W_buoyed = W_air*BF"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_aP33sjk7N_d",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "4563a5f2-01d7-4a9a-abec-2e0791414fd4"
      },
      "source": [
        "print(W_buoyed)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "218345.03816793894\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZT8W6-fj7O9R",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Hydrostatic Pressure at the TOp of Drill collars - \n",
        "\n",
        "p1 = 0.052*mud_w*L_dp"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TdRR8l3x7yrT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "depth = np.arange(12000,-1,-2000)\n",
        "\n",
        "stress = np.arange(-500,501,100) #x1000"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hjMjA-mn_KAe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kEW0ABGs8JV_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#hydrostatic at the bottom of the drill collars is -\n",
        "p2 = 0.052*mud_w*(L_dp+L_dc)"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CWX3Qsej8Kdv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Cross Sectional Area of a 19.5 lbm/ft steel (490lbm/ft) DP is - \n",
        "\n",
        "A_dp = (w_dp/490)*144  #sq. in \n",
        "\n",
        "#CSA of Drill collars \n",
        "\n",
        "A_dc = (w_dc/490)*144  #sq. in\n",
        "\n"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A_Znu6ye9Kmu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Hence following equations can be derived - \n",
        "\n",
        "# A. For 0 < d < 10,000 - F = 218300 - 19.5*depth\n",
        "\n",
        "#B. For d>=10000 - F = 1201000 - 147*depth\n",
        "\n",
        "# F = []\n",
        "\n",
        "# for d in depth:\n",
        "\n",
        "#   if d< 10000:\n",
        "\n",
        "#     f = 218300 - 19.5*d\n",
        "\n",
        "#     F.append(f)\n",
        "\n",
        "#   if d == 10000:\n",
        "\n",
        "#     f = 0 \n",
        "\n",
        "#     F.append(f)\n",
        "  \n",
        "#   if d>10000:\n",
        "\n",
        "\n",
        "#     f = 1201000 - 147*d\n",
        "\n",
        "#     F.append(f)\n",
        "\n"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xvJ7IVvS_TfO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "d_dp = np.arange(10001,-1,-100)\n",
        "\n",
        "d_dc = np.arange (12001,10001,-100)\n",
        "\n",
        "\n",
        "\n",
        "F_dp = 218300 - 19.5*d_dp\n",
        "\n",
        "F_dc = 1201000 - 147*d_dc\n"
      ],
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tqw_rC61GB1j",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "d_0 = [10000,10000,10000]\n",
        "\n",
        "F_0 = [F_dp[0], 0 , F_dc[-1]]\n"
      ],
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "euVs4M5fHsfC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#"
      ],
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gn8Q3PNUBomj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "b5618920-671b-457b-e16c-e01cda032a4b"
      },
      "source": [
        "plt.style.use('default')\n",
        "\n",
        "plt.figure(figsize=(8,6))\n",
        "\n",
        "plt.title('Axial Tensions in a Drill String as a Function of Depth')\n",
        "\n",
        "plt.plot(F_dp,d_dp, label = 'Tension')\n",
        "\n",
        "plt.plot(F_0,d_0, label = 'Neutral')\n",
        "\n",
        "plt.plot(F_dc,d_dc, label = 'Compression')\n",
        "\n",
        "plt.xlabel('Axial Stress, lbf')\n",
        "\n",
        "plt.ylabel('Depth, ft')\n",
        "\n",
        "plt.legend()\n",
        "\n",
        "plt.ylim(12000,0)\n",
        "\n",
        "plt.grid()\n",
        "\n"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TT_wqtO8-mjx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#\n"
      ],
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mNi3aega-nhC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "outputId": "61ac5eca-44c5-47f6-8344-197cbf2dd29a"
      },
      "source": [
        "plt.style.available"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Solarize_Light2',\n",
              " '_classic_test_patch',\n",
              " 'bmh',\n",
              " 'classic',\n",
              " 'dark_background',\n",
              " 'fast',\n",
              " 'fivethirtyeight',\n",
              " 'ggplot',\n",
              " 'grayscale',\n",
              " 'seaborn',\n",
              " 'seaborn-bright',\n",
              " 'seaborn-colorblind',\n",
              " 'seaborn-dark',\n",
              " 'seaborn-dark-palette',\n",
              " 'seaborn-darkgrid',\n",
              " 'seaborn-deep',\n",
              " 'seaborn-muted',\n",
              " 'seaborn-notebook',\n",
              " 'seaborn-paper',\n",
              " 'seaborn-pastel',\n",
              " 'seaborn-poster',\n",
              " 'seaborn-talk',\n",
              " 'seaborn-ticks',\n",
              " 'seaborn-white',\n",
              " 'seaborn-whitegrid',\n",
              " 'tableau-colorblind10']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4jBMdQ-C_YwP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}